Method and apparatus for in-situ detection and isolation of aircraft engine faults

ABSTRACT

A method for performing a fault estimation based on residuals of detected signals includes determining an operating regime based on a plurality of parameters, extracting predetermined noise standard deviations of the residuals corresponding to the operating regime and scaling the residuals, calculating a magnitude of a measurement vector of the scaled residuals and comparing the magnitude to a decision threshold value, extracting an average, or mean direction and a fault level mapping for each of a plurality of fault types, based on the operating regime, calculating a projection of the measurement vector onto the average direction of each of the plurality of fault types, determining a fault type based on which projection is maximum, and mapping the projection to a continuous-valued fault level using a lookup table.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH & DEVELOPMENT

This invention was made with Government support under contract numberNAS3-01135 awarded by the National Aeronautics and Space Administration(NASA). The Government has certain rights in the invention.

BACKGROUND OF THE INVENTION

This invention relates to detecting and classifying faults in anoperating machine, and more particularly to detecting and classifyingfaults in an operating aircraft engine using an Extended Kalman Filterarchitecture.

Aircraft engines must maintain the highest achievable levels ofreliability, because of their extreme safety-critical nature and becausethe vehicles powered by these engines represent enormous investments inresources. However, as with all machinery, small component failures andother operating faults may occur, owing to material failures,environmental disturbances, and normal deterioration during theoperating life of an aircraft engine.

Having faults go undetected and without compensating control actions canrisk further damage and may accelerate deterioration, leading to highersafety risks. Similarly, when engine faults are detected by imprecisemeans and with high levels of uncertainty, operators are often obligedto take the most conservative measures, which typically involve abortinga takeoff or shutting down an engine during flight. Since these measuresin themselves pose some risk to the aircraft and its occupants, it isimportant to be able to distinguish small faults for which more timelyand less extreme measures can safely be taken.

Current engine health monitoring schemes detect only large faults andfailures of the sensors, actuators, and control hardware. Thearchitecture of the engine controls is based either in dual-redundant ortri-redundant hardware. Much of the diagnostic logic depends oncomparing the redundant sensors to each other or to simple static modelsof the sensor. There is no systematic procedure for taking into accountthe behavior of the overall system by using a system model in concertwith all of the available sensors. This causes current methods to beunable to detect faults until they reach a relatively large magnitude.Current monitoring also detects undesired and potentially damagingengine events like stalls and surges, but does not try to isolate thecause of the event.

SUMMARY OF THE INVENTION

An exemplary embodiment of the present invention includes a method forperforming fault estimation based on residuals of detected signals, themethod including comparing the detected signals with estimates of thedetected signals, based on an extended Kalman filter, and outputting theresiduals; and determining a fault type and a fault level by performinghypothesis testing on the residuals.

In another exemplary embodiment of the present invention, there is amethod for performing a fault estimation based on residuals of detectedsignals, including: determining an operating regime based on a pluralityof parameters; extracting predetermined noise standard deviations of theresiduals corresponding to the operating regime and scaling theresiduals; calculating a magnitude of a measurement vector of the scaledresiduals and comparing the magnitude to a decision threshold value;extracting a mean direction and a fault level mapping for each of aplurality of fault types, based on the operating regime; calculating aprojection of the measurement vector onto the mean direction of each ofthe plurality of fault types; determining a fault type based on whichprojection is maximum; and mapping the projection to a continuous-valuedfault level using a lookup table.

In an additional exemplary embodiment of the present invention, there isa computer program product for enabling a computer to implementoperations for performing a fault estimation based on residuals ofdetected signals, the computer program product comprising a computerreadable medium and instructions on the computer readable medium, theoperations including: determining an operating regime based on aplurality of parameters; extracting predetermined noise standarddeviations of the residuals corresponding to the operating regime andscaling the residuals; calculating a magnitude of a measurement vectorof the scaled residuals and comparing the magnitude to a decisionthreshold value; extracting a mean direction and a fault level mappingfor each of a plurality of fault types, based on the operating regime;calculating a projection of the measurement vector onto the meandirection of each of the plurality of fault types; determining a faulttype based on which projection is maximum; and mapping the projection toa continuous-valued fault level using a lookup table.

In another exemplary embodiment of the present invention, there is amethod for detecting and isolating faults in a system, including:detecting a plurality of signals; determining a residual of each of theplurality of signals; determining an operating regime based on aplurality of parameters; extracting predetermined noise standarddeviations of the residuals corresponding to the operating regime andscaling the residuals; calculating a magnitude of a measurement vectorof the scaled residuals and comparing the magnitude to a decisionthreshold value; extracting a mean direction and a fault level mappingfor each of a plurality of fault types, based on the operating regime;calculating a projection of the measurement vector onto the meandirection of each of the plurality of fault types; determining a faulttype based on which projection is maximum; and mapping the projection toa continuous-valued fault level using a lookup table. In a furtherexemplary embodiment of the present invention, there is a computerprogram product for enabling a computer to implement operations fordetecting and isolating faults in a system based on residuals ofdetected signals, the computer program product comprising a computerreadable medium and instructions on the computer readable medium, theoperations including: detecting a plurality of signals; determining aresidual of each of the plurality of signals; determining an operatingregime based on a plurality of parameters; extracting predeterminednoise standard deviations of the residuals corresponding to theoperating regime and scaling the residuals; calculating a magnitude of ameasurement vector of the scaled residuals and comparing the magnitudeto a decision threshold value; extracting a mean direction and a faultlevel mapping for each of a plurality of fault types, based on theoperating regime; calculating a projection of the measurement vectoronto the mean direction of each of the plurality of fault types;determining a fault type based on which projection is maximum; andmapping the projection to a continuous-valued fault level using a lookuptable.

In an additional exemplary embodiment of the present invention, there isan apparatus for detecting and isolating faults in a system based onresiduals of detected signals, the apparatus including: a processorconfigured to determine an operating regime based on a plurality ofparameters; extract predetermined noise standard deviations of theresiduals corresponding to the operating regime and scale the residuals;calculate a magnitude of a measurement vector of the scaled residualsand compare the magnitude to a decision threshold: value; extract a meandirection and a fault level mapping for each of a plurality of faulttypes, based on the operating regime; calculate a projection of themeasurement vector onto the mean direction of each of the plurality offault types; determine a fault type based on which projection ismaximum; and map the projection to a continuous-valued fault level usinga lookup table.

In another exemplary embodiment of the present invention is a system fordetecting and isolating faults based on residuals of detected signals,the system including: a detector which detects the detected signals; anextended Kalman filter which compares the detected signals withestimates of the detected signals and outputs a plurality of residuals;and a processor which performs hypothesis testing on the residuals todetermine a fault type and a fault level.

In another exemplary embodiment of the present invention, there is asystem for detecting and isolating faults based on residuals of detectedsignals, the system including: a detector which detects the detectedsignals; an extended Kalman filter which compares the detected signalswith estimates of the detected signals and outputs a plurality ofresiduals; and a processor configured to determine an operating regimebased on a plurality of parameters; extract predetermined noise standarddeviations of the residuals corresponding to the operating regime andscale the residuals; calculate a magnitude of a measurement vector ofthe scaled residuals and compare the magnitude to a decision thresholdvalue; extract a mean direction and a fault level mapping for each of aplurality of fault types, based on the operating regime; calculate aprojection of the measurement vector onto the mean direction of each ofthe plurality of fault types; determine a fault type based on whichprojection is maximum; and map the projection to a continuous-valuedfault level using a lookup table.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages, nature and various additional features of the inventionwill appear more fully upon consideration of the illustrativeembodiments of the invention which are schematically set forth in thefigures, in which:

FIG. 1 is a diagrammatical representation of a method for performingfault estimation based on residuals of detected signals according to anembodiment of the present invention;

FIG. 2 is a diagrammatical representation of a method for performingfault estimation based on residuals of detected signals according toanother embodiment of the present invention;

FIG. 3 is a diagrammatical representation of a method for detecting andisolating faults in a system according to an embodiment of the presentinvention;

FIG. 4 is a diagrammatical representation of a system for detecting andisolating faults based on residuals of detected signals, according to anembodiment of the present invention; and

FIG. 5 is a diagrammatical representation of data provided by anoperating regime lookup table.

DETAILED DESCRIPTION OF THE INVENTION

The present invention will be explained in further detail by makingreference to the accompanying drawings, which do not limit the scope ofthe invention in any way.

Modern aircraft engines employ full-authority digital controls, whichmake use of sensors deployed throughout the engine. This inventiondescribes how these same sensor measurements can be used to monitor thehealth of the engine, including its actuators and the sensorsthemselves. By using a system model with available sensors, thisinvention is able to isolate the cause of the engine event when itoccurs. Moreover, this invention is able to distinguish small faultsfrom large faults. Along with the safety advantages of being able todistinguish small faults from larger faults, the ability to detect smallfaults early enables more timely engine maintenance, which reduces costsand extends the operating life of the engine.

The invention will now be taught using various exemplary embodiments.Although the embodiments are described in detail, it will be appreciatedthat the invention is not limited to just these embodiments, but has ascope that is significantly broader. The appended claims should beconsulted to determine the true scope of the invention. Prior todescribing the embodiments in detail, however, the meaning of certainterms will be explained.

One embodiment of this invention resides in a computer system. Here, theterm “computer system” is to be understood to include at least a memoryand a processor. In general, the memory will store, at one time oranother, at least portions of an executable program code, and theprocessor will execute one or more of the instructions included in thatexecutable program code. It will be appreciated that the terms“executable program code,” “software,” and “instructions” meansubstantially the same thing for the purposes of this description. It isnot necessary to the practice of this invention that the memory and theprocessor be physically located in the same place. That is to say, it isforeseen that the processor and the memory might be in differentphysical pieces of equipment or even in geographically distinctlocations.

The above-identified invention may be embodied in a computer programproduct, as will now be explained. On a practical level, the softwarethat enables the computer system to perform the operations described indetail further below may be supplied on any of a variety of media.Furthermore, the actual implementation of the approach and operations ofthe invention may actually be statements in a computer language. Suchcomputer language statements, when executed by a computer, cause thecomputer to act in accordance with the particular content of thestatements. Furthermore, the software that enables a computer system toact in accordance with the invention may be provided in any number offorms including, but not limited to, original source code, assemblycode, object code, machine language, compressed or encrypted versions ofthe foregoing, and any and all equivalents now known or hereafterdeveloped.

One familiar with this field will appreciate that “media”, or“computer-readable media”, as used here, may include a diskette, a tape,a compact disc, an integrated circuit, a ROM, a CD/DVD, a cartridge, amemory stick or card, a remote transmission via a communicationscircuit, or any other medium useable by computers, including those nowknown or hereafter developed. For example, to supply software forenabling a computer system to operate in accordance with the invention,the supplier might provide a disc or might transmit the software in someform via satellite transmission, via a direct wired or a wireless link,or via the Internet. Thus, the term, “computer readable medium” isintended to include all of the foregoing and any other medium by whichsoftware may be provided to a processor.

Although the enabling software/code/instructions might be “written on” adisc, “embodied in” an integrated circuit, or “carried over” acommunications circuit, it will be appreciated that, for the purposes ofthis discussion, the software will be referred to simply as being “on”the computer readable medium. Thus, the term “on” is intended toencompass the above mentioned and all equivalent and possible ways inwhich software can be associated with a computer readable medium.

For the sake of simplicity, therefore, the term “program product” isthus used to refer to a computer readable medium, as defined above,which has on it any form of software to enable a computer system tooperate according to any embodiment of the invention.

Having explained the meaning of various terms, the invention will now bedescribed in detail, in the context of a method.

In an exemplary embodiment of the invention, there is a method forperforming fault estimation based on residuals of detected signals. Asillustrated in FIG. 1, the method includes: comparing the detectedsignals with estimates of the detected signals, based on an extendedKalman filter (EKF), and outputting the residuals (step 100); anddetermining a fault type and a fault level by performing hypothesistesting on the residuals (step 101). Examples of the detected signalscorrespond to actual sensor measurements. The EKF compares actual sensormeasurements to estimates provided by an internal model and outputserror signals, i.e., residuals. The EKF is described in the followingreferences: Athans, M. (1996), The Control Handbook, pp. 589-594, CRCPress, United States and Anderson, B. D. O., Moore, J. B., OptimalFiltering, Prentice-Hall, Englewood Cliffs N.J., 1979.

Prior to operation of the exemplary embodiments of the method forperforming fault estimation, a training process is implemented in whichan estimator is trained offline. An engine model is used in the trainingprocess to determine the noise variances and the final values of theresiduals for each of the representative fault types and levels,including the no-fault case. To account for variation in engine behaviorover the flight envelope, which may be defined by ambient temperature,altitude, mach number, and thrust level, the domain of possiblevariation in the flight envelope parameters is divided intorepresentative regimes. The final values of the sensor residuals arelogged for each of these regimes by averaging over all test casespertaining to a particular regime. The sensor noise variance issimilarly segregated by regime. However, the noise is assumed to beindependent of the faults. Thus, the computation of the standarddeviations is performed only on the no-fault training data, aftersegregating the data by regime.

The training process enables computing the noise standard deviations,the mean directions for faults, the mappings between fault levels andvector magnitudes, and the assignment of a decision threshold value. Inan exemplary embodiment, the fault level mappings are stored in a lookuptable, based upon the operating regimes. The training process isrepeated for each sensor, for each fault type, and for each regimecomprising the flight envelope.

In an exemplary embodiment of the invention, the foregoing method isimplemented in an aircraft to detect and isolate errors in the aircraftengine and its associated actuators and sensors. For an engine operatingnormally, the residuals are small and contain only measurement noise.When a fault occurs, the residuals respond in a manner systematic to thetype and severity of the fault. The type of fault and the level of thefault are determined by performing hypothesis testing on the residuals.For example, a Bayesian Hypothesis Test may be performed, in which thelikelihoods of various predefined fault types are assessed given thecurrent residuals. A decision on the fault type is made, which isfollowed by a correlation computation to determine the fault level orseverity.

In the Bayesian Hypothesis Testing, the various fault types and levels,as well as the no-fault condition, are expressed in terms of theirsignatures in a measurement space defined by the scaled EKF residuals,where the scaling is performed by dividing each residual by the standarddeviation of that signal's noise level. This space is of dimension p,where p is the number of sensors. The values attained by the residualswhen the various fault conditions are imposed are thus represented aspositions in the space, and these positions are compared to real-timemeasurements to asses the health of the engine. Bayesian HypothesisTesting is described in the following reference: Van Trees, H. L.,Detection, Estimation, and Modulation Theory, John Wiley & Sons, N.Y.,1968 (Sections 2.1 thru 2.4).

Because the sensors are subjected to noise and unknown biases, the faultconditions give rise not to discrete positions in the measurement space,but rather to probabilistic distributions in the space. More precisely,these are conditional probability functions defined on the p-dimensionalspace, given the various fault hypotheses. If the sensor noise isconsidered to be Gaussian and white, the fault hypotheses may berepresented as ellipsoidal functions centered on various mean positionsin the space, with the axes of the ellipsoid sized according to thenoise variance in each dimension. When the noise is independent acrosssensors, it is customary to normalize the dimensions of the measurementspace by the standard deviation of the corresponding sensor noise, sothat the ellipsoidal probability density functions degenerate tospherical functions of uniform radius. Further, in the absence of apriori knowledge of engine faults, the various fault hypotheses areassumed to be equally likely over a given time interval. Under theseassumptions, the implementation of an optimal Bayesian Hypothesis testthat minimizes the probability of error (false positives, falsenegatives, and misclassifications) is accomplished by means of a simpledistance computation between the real-time residuals and thep-dimensional reference hypotheses, after dividing each residual by thestandard deviation of the sensor noise. This is because the conditionalprobabilities are represented directly by distance, in the standardEuclidian sense, within the normalized measurement space.

In another exemplary embodiment of the present invention, there is acomputer program product for enabling a computer to implement theoperations for performing fault estimation based on residuals ofdetected signals, the computer program product comprising a computerreadable medium and instructions on the computer readable medium, theoperations including: comparing the detected signals with estimates ofthe detected signals, based on an extended Kalman filter (EKF), andoutputting the residuals (step 100); and determining a fault type and afault level by performing hypothesis testing on the residuals (step101), as described above in relation to FIG. 1.

FIG. 2 illustrates an exemplary embodiment of the present invention, inwhich there is a method for performing a fault estimation based onresiduals of detected signals. This method includes: determining anoperating regime from a plurality of parameters (step 201); extractingpredetermined noise standard deviations of the residuals correspondingto the operating regime and scaling the residuals (step 202);calculating a magnitude of a measurement vector of the scaled residuals(step 203); determining if the magnitude is at or above the decisionthreshold value (step 204); extracting a mean direction and a faultlevel mapping for each of a plurality of fault types, based on theoperating regime (step 205); calculating a projection of the measurementvector onto the mean direction of each of the plurality of fault types(step 206); determining a fault type based on which projection ismaximum (step 207); and mapping the projection to a continuous-valuedfault level using a lookup table (step 208). An example of data that canbe provided in an operating regime lookup table is illustrated in FIG.5, which depicts the magnitude of a projection versus severity of fault.However, the operating regime lookup table and the data therein are notlimited to the illustration in FIG. 5.

The detected signals may include any or all of actuator signals, sensorsignals and engine signals.

In step 201, an operating regime is determined from a plurality ofparameters.

In step 202, predetermined noise standard deviations of the residualsare extracted from a source of data, i.e., a lookup table, for example,and the residuals are scaled. In an exemplary embodiment, the scalingincludes normalizing the residuals.

In step 203, the magnitude of the measurement vector of the residuals iscalculated. The measurement vector may be determined by dividing eachresidual by a noise standard deviation. This magnitude is compared tothe decision threshold value, which is a predetermined value. Theselection of the decision threshold value is based upon a balancebetween the false alarm rate and the fault detection rate. Too low adecision threshold value increases the false alarm rate, while too higha decision threshold value increases the likelihood that actual faultswill go undetected.

In step 204, it is determined whether the magnitude is at or above thedecision threshold value. If it is determined that the magnitude isbelow the decision threshold value, it is determined that there is nofault. The operating regime represents one of a plurality of possibleportions of the region in which a signal may be present. The parametersused to determine the operating regime may include flight envelopeparameters. For example, the flight envelope parameters may include, butare not limited to ambient temperature, altitude, mach number, andthrust level.

After it is determined that there is a fault of some type, a meandirection and a fault level mapping are extracted for each of theplurality of fault types based on the operating regime, in step 205. Thefault types may include, but are not limited to, one or more of a sensorfault, an actuator fault, a first machine fault, and a second machinefault. The mean directions are unit vectors approximating the contoursdefined by the end values of the residuals, which can be used in acorrelation computation to determine the fault type. In an exemplaryembodiment, the mean direction for each fault type includes a set ofp-dimensional vectors, where p represents a number of sensors. The faultlevel mapping may be determined via a lookup table that associates faultlevels with length along the appropriate fault contour.

In step 206, a calculation of a projection of the measurement vectoronto the mean direction of each of the plurality of fault types isperformed. Based on a maximum projection of the measurement vector ontothe mean direction of each of the plurality of fault types (step 207),the fault type is determined.

For the determined fault type, the projection is mapped to acontinuous-valued fault level, using a lookup table (step 208). Thecontinuous values may be obtained by interpolating or extrapolating frompredetermined fault levels.

FIG. 3 illustrates a method for detecting and isolating faults in asystem. The method illustrated in FIG. 3 corresponds to the methodillustrated in FIG. 2, but further includes the steps of detectingsignals (step 301) and determining the residuals of the detected signals(step 302). Once steps 301 and 302 are performed, the method of FIG. 3follows the method illustrated in FIG. 2. Since the method of FIG. 2 isdescribed above, the description of these steps is not repeated here.

In another exemplary embodiment of the present invention, there is acomputer program product for enabling a computer to implement operationsfor detecting and isolating faults in a system based on residuals ofdetected signals, the computer program product including a computerreadable medium and instructions on the computer readable medium, theoperations including: detecting signals (step 301); determiningresiduals of the detected signals (step 302); determining an operatingregime from a plurality of parameters (step 201); extractingpredetermined noise standard deviations of the residuals correspondingto the operating regime and scaling the residuals (step 202);calculating a magnitude of a measurement vector of the scaled residuals(step 203); determining if the magnitude is at or above the thresholdvalue (step 204); extracting a mean direction and a fault level mappingfor each of a plurality of fault types, based on the operating regime(step 205); calculating a projection of the measurement vector onto themean direction of each of the plurality of fault types (step 206);determining a fault type based on which projection is maximum (step207); and mapping the projection to a continuous-valued fault levelusing a lookup table (step 208). Since these steps are described above,the description is not repeated here.

FIG. 4 illustrates a block diagram of a system for detecting andisolating faults based on residuals of detected signals, according to anembodiment of the present invention. As shown in FIG. 4, the system 400includes a detector 401 which detects signals; an extended Kalman filter402 which compares the detected signals with estimates of the detectedsignals and outputs a plurality of residuals; and a processor 403 whichperforms hypothesis testing on the residuals to determine a fault typeand a fault level. The processor 403 may be configured to operate inreal time, i.e., during the operation of the system, without introducinga delay into the operation of the system. Also, the hypothesis testingmay be Bayesian Hypothesis Testing.

The detector 401 may include a plurality of sensors 404, which aredisposed in predetermined locations throughout the system 400. Thesensors 404 are configured to monitor a machine, which, in the presentembodiment, is an aircraft engine 405. In a particular embodiment,sensors 404 are disposed on the machine. Sensors 404 may also beconfigured to monitor the actuators 406 or other subsystems within thesystem 400. In particular embodiments, sensors 404 are disposed on theactuators or other subsystems. The engine controller 407 of FIG. 4provides control signals to actuators 406, which control operations ofthe engine 405.

In an exemplary embodiment of the present invention, the processor 403is configured to calculate a magnitude of a measurement vector of theresiduals and compare the magnitude to a decision threshold value;determine an operating regime based on a plurality of parameters;extract a mean direction and a fault level mapping for each of aplurality of fault types, based on the operating regime; calculate aprojection of the measurement vector onto the mean direction of each ofthe plurality of fault types; determine a fault type based on whichprojection is maximum; and map the projection to a continuous-valuedfault level using a lookup table. These operations of the processor aredescribed above in relation to FIG. 2.

While the invention has been described in terms of specific embodiments,those skilled in the art will recognize that the invention can bepracticed with modification within the spirit and scope of the claims.Namely, although the present invention has been discussed in the contextof aircraft engine applications, it is contemplated that the presentinvention can be employed in all applications in which faults of amachine are detected and classified.

1. A method for performing a fault estimation based on a plurality of residuals of a plurality of detected signals from a machine, comprising: determining an operating regime based on a plurality of parameters; extracting predetermined noise standard deviations of said residuals corresponding to said operating regime and scaling said residuals; calculating a magnitude of a measurement vector of said scaled residuals and comparing said magnitude to a decision threshold value; extracting a mean direction and a fault level mapping for each of a plurality of fault types, based on said operating regime; calculating a protection of said measurement vector onto said mean direction of each of said plurality of fault types; determining a fault type based on a maximum projection of said measurement vector onto said mean direction of each of said plurality of fault types; and mapping said projection to a continuous-valued fault level using a lookup table, wherein said calculating the magnitude of the measurement vector and comparing the same to the decision threshold value, determining the fault type based on the maximum projection of the measurement vector, and mapping said projection to the continuous-valued fault level are provided for subsequent use in performing said fault estimation of the machine.
 2. The method of claim 1, wherein, if said magnitude is below said decision threshold value, it is determined that there is no fault.
 3. The method of claim 1, wherein said parameters comprise flight envelope parameters.
 4. The method of claim 1, wherein said scaling comprises normalizing said residuals by dividing said residuals by said noise standard deviations.
 5. The method of claim 1, further comprising correlating said measurement vector with said mean direction of each of said plurality of fault types.
 6. The method of claim 1, wherein said residuals comprise extended Kalman filter residuals.
 7. The method of claim 1, wherein said detected signals comprise at least one of actuator signals, sensor signals, and engine signals.
 8. The method of claim 1, wherein said detected signals comprise actuator signals, sensor signals, and engine signals.
 9. The method of claim 1, wherein said fault types comprise at least one of a sensor fault, an actuator fault, a first machine fault and a second machine fault.
 10. The method of claim 1, wherein said fault types comprise a sensor fault, an actuator fault, a first machine fault and a second machine fault.
 11. The method of claim 1, wherein said mean direction for each fault type comprises a set of p-dimensional vectors, where p represents a number of sensors.
 12. A computer program product for enabling a computer to implement operations for performing a fault estimation based on a plurality of residuals of a plurality of detected signals from a machine, the computer program product comprising a computer readable medium and instructions on the computer readable medium, the operations comprising: determining an operating regime based on a plurality of parameters; extracting predetermined noise standard deviations of said residuals corresponding to said operating regime and scaling said residuals; calculating a magnitude of a measurement vector of said scaled residuals and comparing said magnitude to a decision threshold value; extracting a mean direction and a fault level mapping for each of a plurality of fault types, based on said operating regime; calculating a projection of said measurement vector onto said mean direction of each of said plurality of fault types; determining a fault type based on a maximum projection of said measurement vector onto said mean direction of each of said plurality of fault types; and mapping said projection to a continuous-valued fault level using a lookup table, wherein said calculating the magnitude of the measurement vector and comparing the same to the decision threshold value, determining the fault type based on the maximum projection of the measurement vector, and mapping said projection to the continuous-valued fault level are provided for subsequent use in performing said fault estimation of the machine.
 13. The computer program product of claim 12, wherein, if said magnitude is below said decision threshold value, it is determined that there is no fault.
 14. The computer program product of claim 12, wherein said parameters comprise flight envelope parameters, wherein said detected signals comprise at least one of actuator signals, sensor signals, and engine signals, and said system comprises an engine, wherein said fault types comprise at least one of a sensor fault, an actuator fault, a first machine fault and a second machine fault.
 15. The computer program product of claim 12, wherein said scaling comprises normalizing said residuals by dividing said residuals by said noise standard deviations, and wherein said residuals comprise extended Kalman filter residuals.
 16. The computer program product of claim 12, wherein the operations further comprise: detecting a plurality of signals; and determining a residual of each of said plurality of signals, and wherein the fault estimation is performed for a system that includes an aircraft engine.
 17. A method for detecting and isolating faults in a system, comprising: detecting a plurality of signals; determining a residual of each of said plurality of signals; determining an operating regime based on a plurality of parameters; extracting predetermined noise standard deviations of said residuals corresponding to said operating regime and scaling said residuals; calculating a magnitude of a measurement vector of said scaled residuals and comparing said magnitude to a decision threshold value; extracting a mean direction and a fault level mapping for each of a plurality of fault types, based on said operating regime; calculating a projection of said measurement vector onto said mean direction of each of said plurality of fault types, determining a fault type based on a maximum projection of said measurement vector onto said mean direction of each of said plurality of fault types; and mapping said projection to a continuous-valued fault level using a lookup table wherein said calculating the magnitude of the measurement vector and comparing the same to the decision threshold value, determining the fault type based on the maximum projection of the measurement vector, and mapping said projection to the continuous-valued fault level are provided for subsequent use in performing said fault estimation of the machine.
 18. The method of claim 17, further comprising correlating said measurement vector with said mean direction of each of said plurality of fault types; wherein said measurement vector is determined by dividing each residual by a noise standard deviation; wherein said residuals comprise extended Kalman filter residuals; and wherein, if said magnitude is below said decision threshold value, it is determined that there is no fault.
 19. The method of claim 17, wherein said parameters comprise flight envelope parameters, wherein said detected signals comprise at least one of actuator signals, sensor signals, and engine signals, and said system comprises an engine, wherein said fault types comprise at least one of a sensor fault, an actuator fault, a first machine fault and a second machine fault, and wherein said system comprises an aircraft engine.
 20. An apparatus for detecting and isolating faults in a system based on a plurality of residuals of a plurality of detected signals from the system, said apparatus comprising: a processor configured to determine an operating regime based on a plurality of parameters, extract predetermined noise standard deviations of said residuals corresponding to said operating regime and scale said residuals, calculate a magnitude of a measurement vector of said scaled residuals and compare said magnitude to a decision threshold value, extract a mean direction and a fault level mapping for each of a plurality of fault types, based on said operating regime, calculate a projector of said measurement vector onto said mean direction of each of said plurality of fault types, determine a fault type based on a maximum projection of said measurement vector onto said mean direction of each of said plurality of fault types and map said projection to a continuous-valued fault level using a lookup table, wherein the processor is configured to calculate the magnitude of the measurement vector and compare the same to the decision threshold value, determine the fault type based on the maximum projection of the measurement vector, and map said projection to the continuous-valued fault level for subsequent use in detecting and isolating the faults in the system.
 21. The apparatus of claim 20, wherein said processor is configured to operate in real time.
 22. The apparatus of claim 20, wherein said apparatus is disposed in an aircraft and said system comprises an aircraft engine.
 23. The apparatus of claim 20, wherein said processor comprises an aircraft engine controller.
 24. The apparatus of claim 20, wherein said apparatus is configured to detect and isolate faults in a plurality of actuators, a plurality of sensors, and an engine.
 25. A system for detecting and isolating faults based on a plurality of residuals of a plurality of detected signals, said system comprising: a detector which detects said detected signals; an extended Kalman filter which compares said detected signals with estimates of said detected signals and outputs a plurality of residuals; and a processor which performs hypothesis testing on said residuals to determine a fault type and a fault level, wherein said processor is configured to determine an operating regime based on a plurality of parameters, extract predetermined noise standard deviations of said residuals corresponding to said operating regime and scale said residuals, calculate a magnitude of a measurement vector of said scaled residuals and compare said magnitude to a decision threshold value, extract a mean direction and a fault level mapping for each of a plurality of fault types, based on said operating regime, calculate a projection of said measurement vetcor onto said mean direction of each of said plurality of fault types, determine a fault type based on a maximum projection of said measurement vector onto said mean direction of each of said plurality of fault types, and map said projection to a continuous-valued fault level using a lookup table.
 26. The system of claim 25, wherein said detector comprises a plurality of sensors.
 27. The system of claim 25, wherein said hypothesis testing comprises Bayesian hypothesis testing.
 28. The system of claim 25, wherein said processor is configured to operate in real time.
 29. The system of claim 25, wherein said processor comprises an aircraft engine controller and is disposed in an aircraft and said system comprises an aircraft engine, and wherein said system is configured to detect and isolate faults in a plurality of actuators, a plurality of sensors, and an engine.
 30. A method for performing fault estimation based on a plurality of residuals of a plurality of detected signals from a machine, said method comprising: comparing said detected signals with estimates of said detected signals, based on an extended Kalman filter, and outputting said residuals; and determining a fault type and a fault level by performing hypothesis testing on said residuals, said determining further comprises determining an operating regime based on a plurality of parameters, extracting predetermined noise standard deviations of said residuals corresponding to said operating regime and scale said residuals, calculating a magnitude of a measurement vector of said scaled residuals and compare said magnitude to a decision threshold value, extracting a mean direction and a fault level mapping for each of a plurality of fault types, based on said operating regime, calculating a projection of said measurement vetcor onto said mean direction of each of said plurality of fault types, determining a fault type based on a maximum projection of said measurement vector onto said mean direction of each of said plurality of fault types, and mapping said projection to a continuous-valued fault level using a lookup table, wherein said calculating the magnitude of the measurement vector and comparing the same to the decision threshold value, determining the fault type based on the maximum projection of the measurement vector, and mapping said projection to the continuous-valued fault level are provided for subsequent use in performing said fault estimation of the machine.
 31. The method of claim 30, wherein said hypothesis testing comprises Bayesian hypothesis testing. 